Encapture Revenue (2024)

hey guys recording this here on what is it friday the 19th maybe you're seeing this on monday at the latest but want to let you know we are almost sold out for founder comp sorry founder 500 in austin texas here in about a week uh it's gonna be an amazing event 500 b2b sas founders i'm looking at the attendee list there's almost um there's almost 60 founders with more than six seven million bucks in arr it's an incredible group of group there's over uh there's over a hundred over 150 with more than a million uh more than a million revenue it's an incredible group you don't want to miss it uh grab your hotel grab your flight grab a ticket right now i'll put the link in the bio um in the description here on youtube and i think there's only about three tickets left okay about three tickets left i'd love to see you guys there don't be bashful grab your ticket now hey folks my guest today is will robinson he's the ceo at in capture a high-growth sas platform that helps banks automatically extract important information from documents launched 20 years ago in dallas texas and capture ops companies such as wells fargo frost bank and truist save time and money by using machine learning to process large amounts of data will you ready to take us to the top let's do it nathan thanks for having me on all right you don't look like you can't be that old so this company is founded 20 years ago how old are you yeah i was not the founder that's the short answer uh so that's what's funny this company yes started back in 98 actually 24 years now and i i joined a ceo about three years ago as part of a big transformation that we made as a company the company started as a professional services company that worked and partnered with some legacy automation software companies in our industry and uh those software companies would bring us in and we would help sell and implement their software and over time we started building our own product internally to kind of fill some gaps in the market and um when i joined about three years ago we made uh kind of a big decision to pivot away from these legacy guys and focus purely on the product that we had built um over the previous decades so i had a really fortunate opportunity to step into a business with a lot of folks who knew uh what they're doing knew the market we were selling into and a lot of it was just kind of re-prioritizing um you know uh how we're going to market where we focus making sure we have the right folks on the bus and and what was a team size when you joined in 2019 just to get a sense of the operation yeah it was in the mid 30s 35 5 36 37 folks and um you know it's um yeah we're now up to 75 and uh you know and and look nathan people don't like to talk about this a lot and you know i don't say this as like a um you know this is not a badge of honor but i think transitioning the business was hard and you know um you know making sure that we have the right folks here uh was important and some folks were just like hey you know what i've been working at uh and where you were going it's not not a fit for me and so um we've had you know a lot of change kind of top to bottom in the organization to bring in people that are excited about the vision that we've set and kind of where we're going with our own product so let's fast forward to the product today right so give me a use case what's an example of a document a bank will need your software to extract data from yeah great question so i use the mortgage example a lot because most people have bought a house but when you go talk to a loan officer about buying a house and applying for a mortgage they're going to ask you for a copy of your driver's license a recent pay stub probably the last two years of your tax returns and they're building this financial profile on you to understand how much money do you make and how much of how big of a mortgage can you qualify for typically in a bank there are people in the back office that as you send in those documents they're manually typing in your data they're manually reviewing all the data to make sure it's correct it's accurate you know that if you say you make 80 000 a year your pay stub actually pencils out to an 80 000 a year income our system can come in and automate that entire process so we use machine learning make it easy to collect those documents and once we have them we can read the documents automatically we can extract the data we can do these calculations we can verify that the data is consistent across all the different documents so that people have to spend and candidly waste a lot of time doing that oh what's going on there youtube good to see you guys now imagine this you love watching these interviews with sas founders but imagine if we took all of the valuation data out from over 2807 interviews i've done manually saves you a lot of time well we've done this we've built it into the beautiful interface inside of founder path check this out i'll show you how you can access this in a second but you log in you connect your stripe account you see your valuation real time you can see what it changed over the past 88 days and even set goals for valuation this year now the secret evaluation is there's many different ways to value a sas business so the reason you're going to see three or four different valuations inside of your frowner path dashboard this is all free by the way is because depending on who's doing the buying of your sas company you're going to get a different valuation a vc is going to pay a different valuation private equity firm is different if you're going to do a minority sale that's different and if you sell the whole business that's a different valuation you can see all those when i hover over here right so the teal is what a vc would pay yellow is what private equity and red is if you sold the whole thing outright now what's cool about this is this is not built off random data again you guys hear these interviews on youtube all these datas are built from real-time valuation data points founders share with us on the show so traction 1.2 million seed round 3.7 raised they sold 22 percent of their business go in here and filter by the event maybe you only want to see companies that have sold the whole business well here are a bunch that have been acquired the valuation and the multiple maybe you're going out right now and you're raising your seed round well go in here and look at all this recent seed deals that went down what they raised what valuation they raised at and what percent that they sold there's never been a larger data set of sas valuations than what you can get now inside of founderpath and we're thrilled to bring it to you all right we're gonna go back to the youtube video here in a second but if you want to check this tool out if you want to jump in and sign up you can check it out for free to get your valuation at this link this link founderpath.com forward slash products forward slash evaluations or if you go to founderpath.com and hover over products click on get your valuation here and go ahead and sign up to give it a whirl again all that valuation data live right inside the platform i hope to see you there all right let's jump back into the interview power of ai usually is a direct correlation to the power of the the testing set that was fed the machine in the first place to learn on so it's really hard sometimes to get your your you know a grasp of a large enough testing size to make the ai actually useful what did you guys use to train the ai in the first place what was your testing cohort yeah that's a great that's a great question and it's funny um there's standard documents things like driver's license that are that are very standardized there's a lot of them it's easy to get trained up on what we call you know structured content but documents that come in the same format every time but something like a pay stub there's a lot of variety in that um the the layout where the data is where it's coming from and so you know we've been able to train on hundreds if not thousands of data sets of sample sets to really improve our machine learning and i'll tell you something else nathan kind of a secret in our world when people think of machine learning they think a lot of kind of what they see in commercials which is unsupervised machine learning where you just feed massive data sets into a system and then the system naturally gets you know understands patterns and gets smarter on its own we actually employ a supervised machine learning technique which is where we feed data to the system the system starts to look for patterns and trends and we as humans can come in and actually influence the models that we build and help either you know affirm certain decisions the the system is made or we can correct maybe bad errors or assumptions based on what we see in the data as well understood yeah and so how many on your team of 75 are full-time engineers we've got probably 25 or 30 yeah okay interesting and then i guess give me a sense of sort of how you price so what is your average customer going to pay to use this technology today yeah so we have these broad we we price based on the volume of pages the number of pages or the number of documents that come through our system so if you think about it our the whole value prop of our system is saving you time and money the more uh the more documents that you can run through our system that people don't have to mainly review uh the more value you get out of the system so that's really the that's really the drivers we have these broad-based buckets of really in millions of pages if you think about a lot of these banks are these are high high volume situations and we're pricing it just as a fixed annual fee based on the volume tier you're in the good news is the more volume you send through the platform the incrementally cheaper it gets for you as a customer of ours so we really encourage our customers hey send more and more stuff through let's let's uh you know put more lines of business uh onto our platform and it gets cheaper and that roi uh gets a lot stronger okay that's all we'll understand that but let's answer my question so what what is the sweet spot are we talking like 100 000 year contracts or million dollar contracts or something lower yeah no no it's uh yeah typically hundred hundred fifty two hundred thousand dollars is our starting spot uh we've got several customers spending more than a million dollars a year with us okay so for someone paying you guys more than a million a year how many pages are they probably processing uh you know there it's at least you know it could be 20 30 40 million pages a year 40 million pages per year interesting okay got it so a million dollars would get you 40 million pages sort of processed per year that's for the right ratio value pricing is that roughly yeah yeah we've got guys yeah i mean we've got guys way over a million too so yeah it's it's typically you know again um yeah if you're thinking about it kind of on a per page basis it gets a lot cheaper the more you can scale with us kind of that original that original model training and setup and stop you know in kind of the lower volumes um you really start to scale as you can layer on more and more volume yeah yeah a million divided by 40 million pages what is that like 0.025 cents per page yes or something like that yeah it's pretty inexpensive which is nice yeah interesting okay and then so so launch in 1998 you join in 2019 35 employ i guess i want to get a sense of like sort of what you've done at the company right since that's what you're about to be able to speak to so how many customers was the company working with back when you first joined day one yeah we probably had 15 or 20 on the platform and now we're at uh i think we're gonna cross 50 this year oh wow so help me through that i mean signing up banks is not an easy process that's quite a sales cycle how did you land you know go from 15 to 5-0 yeah a big part is that process like you said banks are they you know they're conservative uh organizations they move slow the big thing for us is helping we're not here to actually sell the software to the bank we're here to help them build the business case for the for the software so it's really funny a lot of our competitors will do like 10 12 15 demos we do like one demo or two demos just to like help people be aware of how the thing works but then we spend a lot of time understanding their business process understanding the opportunities they have for automation we have benchmark data that we have from other clients of ours where we can say okay for this type of loan process here's where we can save you money and then we do a big math exercise that says okay you have this big of a team this is how much time is being spent here's where we can eliminate or reduce certain tasks so here's how here's how much money we can save you every year and um it's it becomes a really powerful sale to you know to bankers who are very uh dollar kind of bottom line numbers driven that makes a lot of sense now how have you capitalized the business are you guys bootstrapped or have you raised capital so we are back by uh by a private equity firm here in dallas so that's when i came on bought out the the original um founders and kind of management team uh they put a little bit of money in in the business but i would say we are running um you know since then we're running kind of more on the conservative scale and kind of organic growth organic funding um we've been able to grow uh very capital efficiently through that and but i would say in the next year or two we'll be in a spot where bring in a new capital partner uh we've just got big dreams there's a huge market here and uh we have a lot we want to go do well okay i guess let's talk pre-private equity because i know that obviously the cap table changes drastically and private equity comes in so pre-private equity had the company raised a bunch of equity a bunch of capital no just been completely bootstrapped by the original founder okay okay got it so his boots dropped by the original founder he was like i'm you know i'm tired i'm getting older i want out whatever the private equity firm comes in now is he still on the capital or the pe firm was it a 100 buyout majority buyout yeah yeah they bought out 100 and so he was that he was he was ready to retire kind of at that stage in life and and so it was a good opportunity for him to get liquidity it was a good opportunity for me to join i had a prior relationship with these guys and uh it was uh you know who was the firm by the way who was the private equity firm called all tourist capital they're they're here in dallas altruist although it's interesting and did they line you up before the deal was done in other words they weren't going to do the deal until they knew the ceo was going to be yeah we had partnered together actually and so i i'd approached them probably a year prior and i have a background in software and tech and i said hey look i'd love to go find a small software business that we can grow together and again i knew these guys um we were really aligned on kind of how we think about investing and values and um and so we kind of came in and did this deal together and that was a big part of doing the deal was hey could i build conviction as the guy stepping in as the ceo on doing this business and creating a lot of value which we were ultimately able yeah interesting and you said this was the firm it's a a a l t r u i s t altruist it's alturus a l t u a-l-t-u-r-u-s o-u-r-u-s got it got it got it very cool now do they have they done this historically a ton sort of sas pe no uh this was one of their first uh they've done a couple technology deals but this was their first sas deal and so that was my background and uh you know they've been super supportive and you know it was a good opportunity um to kind of come in and get them excited about it and i can tell you now they want to do a lot more yeah i was going to say so obviously the motion here usually is organic growth is fine but inner granite growth is way more interesting if you're the hub what are the next six spokes so how are you thinking about m and how much capital do you have at your disposal disposal via altruist to go do a roll-up strategy yeah it's funny we talk about this a lot this is a highly fragmented market so there is a lot of opportunity for m a we have been growing organically so well like a hundred percent year over year for several years and so canada we don't have time or or effort and the equity value we're creating through our organic growth is in the time and that we're able to put into that is worth a lot more than us maybe breaking away from that um and doing m a so i think we want to get a little bit bigger um uh and really kind of prove out our process because i think a lot of the the value in a roll-up is going to be around finding maybe some legacy um uh you know business models that are processing documents either manually or partially manually being able to bring their clients onto our platform and provide a lot of lift for them yeah and then will in terms of sort of scale today um i mean we can sort of estimate based on what you shared with us right five zero customers at a sweet spot of 150k per year obviously with a caveat that sounds like you have some whales in there right some million dollar plus accounts but if we take 150k a year times 50 that puts you at a minimum of about seven million in terms of run rate is that fair you guys are north of that at this point we are north yeah 150s are minimum so yeah average deal size you know we're yeah i'd say we're probably 4 to 500k average deal size oh got it so you're much bro i mean you're you're pushing like 30 35 million in ar then something much bigger no we're no i think no we're not that we're not there yet okay can you break 30 million this year i would love to no it's probably probably 18 24 months out all right fair enough fair enough um i guess last question i've got um before we wrap up with the famous five you know guy like you again how old are you i'm 35. i'm just guessing right look at the young guy right so how does a pe from sort of convince you to come in and do this when you could go you know what i'm young i've got energy i could just go start my own thing from scratching 100 yeah i had to convince them actually nathan um that was my strategy is you know i it's part of it like figuring out yourself and kind of what are you good at and i had done some really early stage stuff or been involved in that and i felt like i do best in my experience when i have something to start with and grow um and so kind of that zero to one i was like hey start me at the one and let me take the one to five or the one to ten and so you know that was the model they were comfortable with i think you know they're the type that don't want to do the zero to one um and so we were able to come into this business we had enough traction we had a product market fit there was a lot of work to do around kind of repositioning vision and culture and strategy and people and process but in terms of the the pain we were solving for our customers and having enough traction we felt very confident that that we could do something with this and when you look at your success so far the past three years what was on a percent basis what's total revenue growth since 2019 up through today well let's see inc the inc 5000 came out yesterday and i think that's a three year period and i think we're at 270 percent so that's probably okay that's probably close to what it would be so does this inc 5000 you have to do do they publish the revenue figure no just they don't just the percentage got it 270. got it well look if you're north of 8 million and south of 30 million you can sort of back into something right but 270 growth is impressive um would you argue that most of that revenue growth has come from adding again those new customers or is it more from getting the current customers to process more pages per year i think it's been both you know one thing we had to validate coming in was um with our historic model we hadn't done a good job of going out finding new customers we had been relying a lot on these legacy customers from legacy partners to to sell our product and so it was really a two-prong approach let's go back to our existing customers and again we we had a bunch of r d to do on the machine learning side to really get the product to where i felt like we needed to go and get full value out of it and so a lot of it was us you know adding these capabilities going back to the existing customers saying hey this is going to provide a ton of value let's you know let's get you guys um you know spending candidly spending a lot more money with us but doing a lot more with us which resulted in a lot of upsell and then you know restarting or really starting from scratch kind of our our go to market um for net new logos and so it's been it's been a balance i would say in the early days it was a lot of working with existing customers while we got our go to market figured out and now we've got a really nice legion engine uh for new logos what is legion where do you get leads from it's a heavy outbound and heavy event driven that's been the best for us if you think about our deal size and you think about who we're selling to um there's a very targeted group of people who buy what we what we sell and they're not you know it's it's not it's a very enterprise b2b motion so we spend a lot of time and effort getting to know people finding the right people crafting uh very kind of curated messaging towards them um events have been great especially kind of coming back from over the last year coming back from kobe people really want to be it be at these events and see what's out there so uh that's been that's been our strategy so far we probably need to diversify and will but it's um it's one of those if it ain't broke don't fix it kind of situations all right cool let's wrap up here with the famous five number one will what's your favorite business book um man about six well i'll say my most recent that i love it's called no ego and it was about how to build high performance teams at how to make sure that people are thinking about your business thinking about teams in the right way having a positive assumptions about what they do we actually have the whole company read it and uh i don't have favorite business books because i read a ton of them and i feel like i get a lot out of everything um that's the most recent i will say this my the the most impressionable one i ever read was probably 10 years ago it's called servant leadership it was written by this guy robert greenleaf back in the 70s uh he'd been a long time ibm guy and then went to academia and he talked about kind of successful traits of servant leaders that's really been um probably a core of how i try to run the business where i'm not the guy at the top i'm the guy at the bottom and my whole job is to empower people around me to be successful in what they do number two is our ceo you're following or studying i feel like these days it's just what not to do it's i won't name names but uh it's folks you can read about the paper uh i think that's been my key thing over the last six to nine months is watching kind of spectacular implosions of certain especially high growth software companies of how not to lay people off for example um how not to uh maybe leave your current company and go to a new company so i'll leave it at that i think that's been the most interesting for me is i feel like we got a good thing going here we got a good we got a good culture um don't screw it up number three what's your favorite online tool for building in capture say that one again online tool favorite online tool slack i guess i mean it just keeps us so connected no that's good number four how many hours of sleep to get every night oh this is a big one i get eight to nine that's good and situation married single kids uh married two kids one on the way so oh very cool so three total like three with the one on the way two uh uh three no i have two kids and one on the way so got it so three total here shortly yeah that's right very cool and 35 years old last question something you wish you when you were 20. that uh career paths are not linear uh the most successful people in the world have very winding unusual paths and it's okay to hold on to things loosely and just let it happen guys there you have it in capture.com legacy player in the bank space uh services and software to the banking space launched in 1998 will came in when a private equity firm came in and bought the firm altruist capital bought it in uh 2019 he came in he sent scorn at 270 the company today is now serving 50 customers at an average price point somewhere between 250k and 500k you know revenue call between sort of 10 and 20 million bucks he looks continued to scale um doing it very efficiently no outside capital raised since that private equity deal it was 100 deal back in 2019 75 on the team today 25 engineers obviously heavy engineering when they're doing machine learning and ai helping banks process documents faster their biggest customers are processing caught 40 50 million documents per year well thanks for taking us to the top awesome thanks nathan one more thing before you go we have a brand new show every thursday at 1 pm central it's called shark tank for sas we call it deal or bust one founder comes on three hungry buyers they try and do a deal live and the founder shares back end dashboards their expenses their revenue arpu cac ltv you name it they share it and the buyers try and make a deal live it is fun to watch every thursday 1 pm central 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